app.py

import os
import glob
import gradio as gr
import shutil
from ultralytics import YOLO

# Initialize YOLO model
model = YOLO('./best_train2.pt')

# Class to color mapping
ClassToColorMapping = {
    0: "black",
    1: "gray",
    2: "green",
    3: "purple",
    4: "red"
}

def check_ffmpeg():
    """Check if ffmpeg is installed."""
    if shutil.which("ffmpeg") is None:
        raise EnvironmentError("ffmpeg is not installed or not found in the system PATH. Please install ffmpeg to proceed.")

def convert_to_mp4(file):
    """Convert a .avi video file to .mp4 format."""
    mp4_file = f"{file[:-4]}.mp4"
    os.system(f"ffmpeg -y -i \"{file}\" -vcodec libx264 \"{mp4_file}\"")
    os.remove(file)  # Remove the original .avi file after conversion
    return mp4_file

def process_video(video_path, output_option):
    # Check if ffmpeg is installed
    check_ffmpeg()

    # Determine if the video needs to be saved
    save_video = output_option in ["Video", "Both"]

    # Run the YOLO model on the video, specifying the tracker configuration and save option
    results = model.track(
        video_path,
        save=save_video,
        tracker="bytetrack.yaml",
        half=False,
        vid_stride=1,
        iou=0.75,
        conf=0.25
    )

    # Initialize a dictionary to store unique IDs for each color
    chip_ids = {color: set() for color in ClassToColorMapping.values()}

    # Check if results are None or if there are no results
    if results is None or len(results) == 0:
        raise Exception("No detections were found in the video.")

    # Track the unique IDs for each color
    for result in results:
        # Check if result.boxes exists and has valid id and cls attributes
        if result.boxes is None or result.boxes.id is None or result.boxes.cls is None:
            continue  # Skip if no boxes or if ids or classes are None

        # Check if the id and cls tensors are not empty
        if result.boxes.id.numel() == 0 or result.boxes.cls.numel() == 0:
            continue  # Skip if ids or classes are empty tensors

        for cls, id_ in zip(result.boxes.cls, result.boxes.id):
            if cls is None or id_ is None:
                continue  # Skip if class or id is None
            color = ClassToColorMapping.get(int(cls.item()), None)  # Map class label to color
            if color:
                chip_ids[color].add(int(id_.item()))  # Add the unique ID to the set

    # Convert sets to counts of unique IDs
    chip_counts_by_color = {color: len(ids) for color, ids in chip_ids.items()}

    # If the user only wants the count, return it immediately
    if output_option == "Count":
        return chip_counts_by_color, None

    # Process video saving if required
    if save_video:
        video_name = os.path.splitext(os.path.basename(video_path))[0]

        # Find the latest directory created in 'runs/detect/'
        output_dir = max(glob.glob('./runs/detect/*'), key=os.path.getmtime)

        # Find the saved video file in the latest directory with the specific video name
        video_files = glob.glob(os.path.join(output_dir, f'{video_name}*.avi'))

        if not video_files:
            raise Exception(f"No .avi video files found in directory {output_dir} for {video_name}")

        # Convert each .avi video to .mp4
        mp4_files = []
        for file in video_files:
            mp4_file = convert_to_mp4(file)
            mp4_files.append(mp4_file)

        matched_mp4_files = [file for file in mp4_files if video_name in file]

        if not matched_mp4_files:
            raise Exception(f"No .mp4 video files found in directory {output_dir} after conversion for {video_name}.")

        # Return video path and chip counts if both are requested
        if output_option == "Both":
            return chip_counts_by_color, matched_mp4_files[0]
        else:
            return None, matched_mp4_files[0]
    else:
        # Return only chip counts if no video is needed
        return chip_counts_by_color, None

# Define Gradio inputs
video_input = gr.Video()
output_option_input = gr.Radio(choices=["Count", "Video", "Both"], label="Select output type", value="Both")  # Set default value to "Both"

# Define a single example for the interface
examples = [
    [os.path.abspath("video_1.mp4"), "Both"]
]

video_interface = gr.Interface(
    fn=process_video,
    inputs=[video_input, output_option_input],
    outputs=[gr.JSON(label="Color Counts"), "video"],  # Ensure two outputs are defined, using JSON for color counts
    title="YOLO Video Tracking Application with Color Counting",
    description="A simple application to track objects in a video using YOLO model and count unique objects by color. Upload your own video, or click one of the examples to load them.",
    article="""<div>
                <p style="text-align: center">Upload a video file and select the type of output you want: unique object counts by color, processed video, or both. Then, hit submit to process the video.</p>
               </div>""",
    examples=examples,
    cache_examples=True  # Disable caching to speed up launch
)

# Deploy the interface with share enabled
gr.TabbedInterface([video_interface], ["Track Video"]).launch(share=True)